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time delay neural network
Time delay neural network (TDNN) 〔Alexander Waibel et al, ''Phoneme Recognition Using Time-Delay Neural Networks'' IEEE Transactions on Acoustics, Speech and Signal Processing, Volume 37, No. 3, pp. 328. - 339 March 1989.〕 is an artificial neural network architecture whose primary purpose is to work on sequential data. The TDNN units recognise features independent of time-shift (i.e. sequence position) and usually form part of a larger pattern recognition system. Converting continuous audio into a stream of classified phoneme labels for speech recognition.
An input signal is augmented with delayed copies as other inputs, the neural network is time-shift invariant since it has no internal state.
The original paper presented a perceptron network whose connection weights were trained with the back-propagation algorithm, this may be done in batch or online. The Stuttgart Neural Network Simulator〔(TDNN Fundamentals ), Kapitel aus dem Online Handbuch des SNNS〕 implements that version.
==See also==
* Convolutional neural network - a convolutional neural net where the convolution is performed along the time axis of the data is very similar to a TDNN.
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